Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy
ZHE Zhang, X Wei - Seminars in Cancer Biology, 2023 - Elsevier
The rapid development of artificial intelligence (AI) technologies in the context of the vast
amount of collectable data obtained from high-throughput sequencing has led to an …
amount of collectable data obtained from high-throughput sequencing has led to an …
Deep machine learning for medical diagnosis, application to lung cancer detection: a review
HT Gayap, MA Akhloufi - BioMedInformatics, 2024 - mdpi.com
Deep learning has emerged as a powerful tool for medical image analysis and diagnosis,
demonstrating high performance on tasks such as cancer detection. This literature review …
demonstrating high performance on tasks such as cancer detection. This literature review …
Long-term cancer survival prediction using multimodal deep learning
LA Vale-Silva, K Rohr - Scientific Reports, 2021 - nature.com
The age of precision medicine demands powerful computational techniques to handle high-
dimensional patient data. We present MultiSurv, a multimodal deep learning method for long …
dimensional patient data. We present MultiSurv, a multimodal deep learning method for long …
Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study
Background Peritoneal recurrence is the predominant pattern of relapse after curative-intent
surgery for gastric cancer and portends a dismal prognosis. Accurate individualised …
surgery for gastric cancer and portends a dismal prognosis. Accurate individualised …
A versatile deep learning architecture for classification and label-free prediction of hyperspectral images
Hyperspectral imaging is a technique that provides rich chemical or compositional
information not regularly available to traditional imaging modalities such as intensity …
information not regularly available to traditional imaging modalities such as intensity …
Foundation model for cancer imaging biomarkers
Foundation models in deep learning are characterized by a single large-scale model trained
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …
Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation
We are rapidly approaching a future in which cancer patient digital twins will reach their
potential to predict cancer prevention, diagnosis, and treatment in individual patients. This …
potential to predict cancer prevention, diagnosis, and treatment in individual patients. This …
Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size
requirements and interpretability issues. Using a pretrained DL model through a radiomics …
requirements and interpretability issues. Using a pretrained DL model through a radiomics …
Multi-transsp: Multimodal transformer for survival prediction of nasopharyngeal carcinoma patients
Nasopharyngeal carcinoma (NPC) is a malignant tumor that often occurs in Southeast Asia
and southern China. Since there is a need for a more precise personalized therapy plan that …
and southern China. Since there is a need for a more precise personalized therapy plan that …
[HTML][HTML] Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning
Background and purpose The aim of this study was to develop and evaluate a prediction
model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) …
model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) …